{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db305245",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From E:\\001_tem_files\\06_c_temp\\ipykernel_23208\\4090275367.py:18: The name tf.logging.set_verbosity is deprecated. Please use tf.compat.v1.logging.set_verbosity instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 环境设置与导入\n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "import numpy as np\n",
    "import warnings\n",
    "\n",
    "# ------------------- 警告抑制 -------------------\n",
    "warnings.filterwarnings('ignore') # 忽略所有警告\n",
    "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) # 抑制TensorFlow日志\n",
    "\n",
    "# ------------------- Matplotlib 中文显示设置 -------------------\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei', 'Arial Unicode MS'] \n",
    "plt.rcParams['axes.unicode_minus'] = False \n",
    "\n",
    "# ------------------- 配置 -------------------\n",
    "# 创建输出目录\n",
    "if not os.path.exists('output'):\n",
    "    os.makedirs('output')\n",
    "\n",
    "# 数据文件路径\n",
    "TRAIN_FILE = 'iris_training.csv'\n",
    "TEST_FILE = 'iris_test.csv'\n",
    "\n",
    "# 定义类别名称 (Iris数据集通常有3个类别)\n",
    "CLASS_NAMES = ['setosa', 'versicolor', 'virginica']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f10f5056",
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据加载与模型构建函数\n",
    "\n",
    "def load_data(filepath, feature_cols):\n",
    "    \"\"\"\n",
    "    加载CSV数据并分离特征和标签。\n",
    "    根据Iris数据集的通用知识，前4列是特征，第5列是类别标签。\n",
    "    跳过CSV文件的第一行，因为它通常是说明信息。\n",
    "    \"\"\"\n",
    "    col_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']\n",
    "    df = pd.read_csv(filepath, header=None, names=col_names, skiprows=1)\n",
    "\n",
    "    X = df[feature_cols]\n",
    "    # 将字符串类别标签转换为数字（0, 1, 2）\n",
    "    # 注意：这里假设标签已经是0, 1, 2或者可以通过to_numeric直接转换\n",
    "    # 如果是字符串，需要使用LabelEncoder\n",
    "    y = pd.to_numeric(df['species'], errors='coerce').fillna(0).astype(np.int64)\n",
    "    return X, y\n",
    "\n",
    "def build_model(input_shape, num_classes):\n",
    "    \"\"\"\n",
    "    构建、编译并返回一个Keras模型。\n",
    "    使用两个隐藏层和一个Softmax输出层。\n",
    "    \"\"\"\n",
    "    model = Sequential([\n",
    "        Dense(10, activation='relu', input_shape=(input_shape,)),\n",
    "        Dense(10, activation='relu'),\n",
    "        Dense(num_classes, activation='softmax') # Softmax激活函数用于多分类\n",
    "    ])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b52c20de",
   "metadata": {},
   "outputs": [],
   "source": [
    "#实验执行函数\n",
    "\n",
    "def run_experiment(hyperparams):\n",
    "    \"\"\"\n",
    "    使用给定的超参数运行一次完整的训练和评估实验。\n",
    "    包括数据加载、标准化、模型构建、训练和评估。\n",
    "    \"\"\"\n",
    "    feature_combination = hyperparams['feature_combination']\n",
    "    learning_rate = hyperparams['learning_rate']\n",
    "    epochs = hyperparams['epochs']\n",
    "    \n",
    "    print(f\"--- 开始实验: 特征={feature_combination}, LR={learning_rate}, Epochs={epochs} ---\")\n",
    "\n",
    "    # 加载数据\n",
    "    X_train_full, y_train_full = load_data(TRAIN_FILE, feature_combination)\n",
    "    X_test, y_test = load_data(TEST_FILE, feature_combination)\n",
    "\n",
    "    # 标准化特征\n",
    "    scaler = StandardScaler()\n",
    "    X_train_scaled = scaler.fit_transform(X_train_full)\n",
    "    X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "    # 构建和编译模型\n",
    "    model = build_model(input_shape=len(feature_combination), num_classes=len(CLASS_NAMES))\n",
    "    optimizer = Adam(learning_rate=learning_rate)\n",
    "    model.compile(optimizer=optimizer,\n",
    "                  loss='sparse_categorical_crossentropy', # 交叉熵损失函数\n",
    "                  metrics=['accuracy'])\n",
    "\n",
    "    # 训练模型\n",
    "    history = model.fit(X_train_scaled, y_train_full,\n",
    "                        epochs=epochs,\n",
    "                        validation_data=(X_test_scaled, y_test),\n",
    "                        verbose=0) # 设置为0以减少日志输出\n",
    "\n",
    "    # 评估模型\n",
    "    train_loss, train_acc = model.evaluate(X_train_scaled, y_train_full, verbose=0)\n",
    "    test_loss, test_acc = model.evaluate(X_test_scaled, y_test, verbose=0)\n",
    "    \n",
    "    print(f\"--- 实验结束: 训练准确率={train_acc:.4f}, 测试准确率={test_acc:.4f} ---\")\n",
    "\n",
    "    # 记录结果\n",
    "    result = {\n",
    "        'features': '+'.join(feature_combination),\n",
    "        'num_features': len(feature_combination),\n",
    "        'learning_rate': learning_rate,\n",
    "        'epochs': epochs,\n",
    "        'train_loss': train_loss,\n",
    "        'train_accuracy': train_acc,\n",
    "        'test_loss': test_loss,\n",
    "        'test_accuracy': test_acc,\n",
    "        'history': history.history # 保存训练历史用于绘图\n",
    "    }\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9c8bdeea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "将运行 99 个实验。\n"
     ]
    }
   ],
   "source": [
    "#定义超参数网格与实验列表\n",
    "# 定义鸢尾花的四种特征名称\n",
    "feature_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']\n",
    "\n",
    "# 生成不同数量特征的组合 (2种、3种、4种)\n",
    "feature_combinations = []\n",
    "for i in [2, 3, 4]:\n",
    "    feature_combinations.extend(itertools.combinations(feature_names, i))\n",
    "\n",
    "# 定义要测试的学习率和迭代次数选项\n",
    "learning_rates = [0.01, 0.005, 0.001]\n",
    "epochs_options = [50, 100, 150]\n",
    "\n",
    "# 构建所有超参数组合的实验列表\n",
    "experiments = []\n",
    "for features in feature_combinations:\n",
    "    for lr in learning_rates:\n",
    "        for epochs in epochs_options:\n",
    "            experiments.append({\n",
    "                'feature_combination': list(features),\n",
    "                'learning_rate': lr,\n",
    "                'epochs': epochs\n",
    "            })\n",
    "\n",
    "print(f\"将运行 {len(experiments)} 个实验。\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "339f808d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- 开始实验: 特征=['sepal_length', 'sepal_width'], LR=0.01, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.8417, 测试准确率=0.8000 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width'], LR=0.01, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.8333, 测试准确率=0.6667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width'], LR=0.01, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.8500, 测试准确率=0.7667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width'], LR=0.005, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.8333, 测试准确率=0.7667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width'], LR=0.005, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.8417, 测试准确率=0.7333 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width'], LR=0.005, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.8333, 测试准确率=0.7333 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width'], LR=0.001, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.7917, 测试准确率=0.6667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width'], LR=0.001, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.7917, 测试准确率=0.6667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width'], LR=0.001, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.8167, 测试准确率=0.8000 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'petal_length'], LR=0.01, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.9750, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'petal_length'], LR=0.01, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.9583, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'petal_length'], LR=0.01, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9750, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'petal_length'], LR=0.005, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.9583, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'petal_length'], LR=0.005, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.9583, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'petal_length'], LR=0.005, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9583, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'petal_length'], LR=0.001, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.6917, 测试准确率=0.6000 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'petal_length'], LR=0.001, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.8167, 测试准确率=0.8333 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'petal_length'], LR=0.001, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9167, 测试准确率=0.9000 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'petal_width'], LR=0.01, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.9583, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'petal_width'], LR=0.01, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.9750, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'petal_width'], LR=0.01, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9667, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'petal_width'], LR=0.005, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.9500, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'petal_width'], LR=0.005, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.9667, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'petal_width'], LR=0.005, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9583, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'petal_width'], LR=0.001, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.8083, 测试准确率=0.7000 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'petal_width'], LR=0.001, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.9167, 测试准确率=0.8333 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'petal_width'], LR=0.001, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9333, 测试准确率=0.9333 ---\n",
      "--- 开始实验: 特征=['sepal_width', 'petal_length'], LR=0.01, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.9500, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_width', 'petal_length'], LR=0.01, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.9583, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_width', 'petal_length'], LR=0.01, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9583, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_width', 'petal_length'], LR=0.005, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.9500, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_width', 'petal_length'], LR=0.005, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.9583, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_width', 'petal_length'], LR=0.005, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9583, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_width', 'petal_length'], LR=0.001, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.8167, 测试准确率=0.7000 ---\n",
      "--- 开始实验: 特征=['sepal_width', 'petal_length'], LR=0.001, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.9250, 测试准确率=0.9000 ---\n",
      "--- 开始实验: 特征=['sepal_width', 'petal_length'], LR=0.001, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9500, 测试准确率=1.0000 ---\n",
      "--- 开始实验: 特征=['sepal_width', 'petal_width'], LR=0.01, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.9500, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_width', 'petal_width'], LR=0.01, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.9500, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_width', 'petal_width'], LR=0.01, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9500, 测试准确率=0.9333 ---\n",
      "--- 开始实验: 特征=['sepal_width', 'petal_width'], LR=0.005, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.9500, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_width', 'petal_width'], LR=0.005, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.9500, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_width', 'petal_width'], LR=0.005, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9583, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_width', 'petal_width'], LR=0.001, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.8667, 测试准确率=0.8000 ---\n",
      "--- 开始实验: 特征=['sepal_width', 'petal_width'], LR=0.001, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.8833, 测试准确率=0.8333 ---\n",
      "--- 开始实验: 特征=['sepal_width', 'petal_width'], LR=0.001, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9500, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['petal_length', 'petal_width'], LR=0.01, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.9583, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['petal_length', 'petal_width'], LR=0.01, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.9583, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['petal_length', 'petal_width'], LR=0.01, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9667, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['petal_length', 'petal_width'], LR=0.005, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.9500, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['petal_length', 'petal_width'], LR=0.005, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.9583, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['petal_length', 'petal_width'], LR=0.005, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9583, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['petal_length', 'petal_width'], LR=0.001, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.9417, 测试准确率=0.9333 ---\n",
      "--- 开始实验: 特征=['petal_length', 'petal_width'], LR=0.001, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.9417, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['petal_length', 'petal_width'], LR=0.001, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9583, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width', 'petal_length'], LR=0.01, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.9750, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width', 'petal_length'], LR=0.01, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.9750, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width', 'petal_length'], LR=0.01, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9833, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width', 'petal_length'], LR=0.005, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.9750, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width', 'petal_length'], LR=0.005, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.9667, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width', 'petal_length'], LR=0.005, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9833, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width', 'petal_length'], LR=0.001, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.8250, 测试准确率=0.7667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width', 'petal_length'], LR=0.001, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.8167, 测试准确率=0.7333 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width', 'petal_length'], LR=0.001, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9667, 测试准确率=0.9333 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width', 'petal_width'], LR=0.01, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.9667, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width', 'petal_width'], LR=0.01, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.9667, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width', 'petal_width'], LR=0.01, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9667, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width', 'petal_width'], LR=0.005, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.9583, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width', 'petal_width'], LR=0.005, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.9750, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width', 'petal_width'], LR=0.005, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9750, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width', 'petal_width'], LR=0.001, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.8500, 测试准确率=0.6667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width', 'petal_width'], LR=0.001, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.8750, 测试准确率=0.8667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width', 'petal_width'], LR=0.001, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9500, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'petal_length', 'petal_width'], LR=0.01, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.9750, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'petal_length', 'petal_width'], LR=0.01, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.9750, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'petal_length', 'petal_width'], LR=0.01, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9750, 测试准确率=1.0000 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'petal_length', 'petal_width'], LR=0.005, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.9667, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'petal_length', 'petal_width'], LR=0.005, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.9750, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'petal_length', 'petal_width'], LR=0.005, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9750, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'petal_length', 'petal_width'], LR=0.001, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.8500, 测试准确率=0.8000 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'petal_length', 'petal_width'], LR=0.001, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.9583, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'petal_length', 'petal_width'], LR=0.001, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9667, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_width', 'petal_length', 'petal_width'], LR=0.01, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.9833, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_width', 'petal_length', 'petal_width'], LR=0.01, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.9833, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_width', 'petal_length', 'petal_width'], LR=0.01, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9917, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_width', 'petal_length', 'petal_width'], LR=0.005, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.9833, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_width', 'petal_length', 'petal_width'], LR=0.005, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.9833, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_width', 'petal_length', 'petal_width'], LR=0.005, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9917, 测试准确率=0.9333 ---\n",
      "--- 开始实验: 特征=['sepal_width', 'petal_length', 'petal_width'], LR=0.001, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.9000, 测试准确率=0.8000 ---\n",
      "--- 开始实验: 特征=['sepal_width', 'petal_length', 'petal_width'], LR=0.001, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.9167, 测试准确率=0.9000 ---\n",
      "--- 开始实验: 特征=['sepal_width', 'petal_length', 'petal_width'], LR=0.001, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9667, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'], LR=0.01, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.9833, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'], LR=0.01, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.9917, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'], LR=0.01, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9917, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'], LR=0.005, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.9750, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'], LR=0.005, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.9917, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'], LR=0.005, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9917, 测试准确率=0.9667 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'], LR=0.001, Epochs=50 ---\n",
      "--- 实验结束: 训练准确率=0.8500, 测试准确率=0.7333 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'], LR=0.001, Epochs=100 ---\n",
      "--- 实验结束: 训练准确率=0.8833, 测试准确率=0.9333 ---\n",
      "--- 开始实验: 特征=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'], LR=0.001, Epochs=150 ---\n",
      "--- 实验结束: 训练准确率=0.9500, 测试准确率=0.9667 ---\n",
      "\n",
      "所有实验结果DataFrame的前5行:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>num_features</th>\n",
       "      <th>learning_rate</th>\n",
       "      <th>epochs</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>test_loss</th>\n",
       "      <th>test_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sepal_length+sepal_width</td>\n",
       "      <td>2</td>\n",
       "      <td>0.010</td>\n",
       "      <td>50</td>\n",
       "      <td>0.327356</td>\n",
       "      <td>0.841667</td>\n",
       "      <td>0.421383</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sepal_length+sepal_width</td>\n",
       "      <td>2</td>\n",
       "      <td>0.010</td>\n",
       "      <td>100</td>\n",
       "      <td>0.304617</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.523251</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sepal_length+sepal_width</td>\n",
       "      <td>2</td>\n",
       "      <td>0.010</td>\n",
       "      <td>150</td>\n",
       "      <td>0.316527</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.471934</td>\n",
       "      <td>0.766667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sepal_length+sepal_width</td>\n",
       "      <td>2</td>\n",
       "      <td>0.005</td>\n",
       "      <td>50</td>\n",
       "      <td>0.367287</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.460239</td>\n",
       "      <td>0.766667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sepal_length+sepal_width</td>\n",
       "      <td>2</td>\n",
       "      <td>0.005</td>\n",
       "      <td>100</td>\n",
       "      <td>0.335847</td>\n",
       "      <td>0.841667</td>\n",
       "      <td>0.448838</td>\n",
       "      <td>0.733333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   features  num_features  learning_rate  epochs  train_loss  \\\n",
       "0  sepal_length+sepal_width             2          0.010      50    0.327356   \n",
       "1  sepal_length+sepal_width             2          0.010     100    0.304617   \n",
       "2  sepal_length+sepal_width             2          0.010     150    0.316527   \n",
       "3  sepal_length+sepal_width             2          0.005      50    0.367287   \n",
       "4  sepal_length+sepal_width             2          0.005     100    0.335847   \n",
       "\n",
       "   train_accuracy  test_loss  test_accuracy  \n",
       "0        0.841667   0.421383       0.800000  \n",
       "1        0.833333   0.523251       0.666667  \n",
       "2        0.850000   0.471934       0.766667  \n",
       "3        0.833333   0.460239       0.766667  \n",
       "4        0.841667   0.448838       0.733333  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 执行所有实验并显示部分结果\n",
    "\n",
    "# 运行所有实验\n",
    "all_results = [run_experiment(params) for params in experiments]\n",
    "\n",
    "# 将所有实验结果转换为DataFrame\n",
    "results_df = pd.DataFrame(all_results)\n",
    "\n",
    "print(\"\\n所有实验结果DataFrame的前5行:\")\n",
    "display(results_df.drop('history', axis=1).head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b859c702",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "========================= 实验完成 =========================\n",
      "所有实验结果已保存到 output/all_experiment_results.csv\n",
      "\n",
      "--- 最佳实验结果 ---\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "features         sepal_width+petal_length\n",
       "learning_rate                       0.001\n",
       "epochs                                150\n",
       "test_accuracy                         1.0\n",
       "Name: 35, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#分析并显示最佳整体实验结果\n",
    "\n",
    "# 找出最佳结果 (基于测试集准确率最高的行)\n",
    "best_result_row = results_df.loc[results_df['test_accuracy'].idxmax()]\n",
    "\n",
    "# 保存所有结果到CSV文件 (不包括训练历史，因为它是一个字典)\n",
    "results_df.drop('history', axis=1).to_csv('output/all_experiment_results.csv', index=False)\n",
    "\n",
    "print(\"\\n========================= 实验完成 =========================\")\n",
    "print(\"所有实验结果已保存到 output/all_experiment_results.csv\")\n",
    "\n",
    "print(\"\\n--- 最佳实验结果 ---\")\n",
    "display(best_result_row[['features', 'learning_rate', 'epochs', 'test_accuracy']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "92406a93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "最佳模型的训练历史图已保存到 output/best_model_training_history.png\n"
     ]
    }
   ],
   "source": [
    "#可视化最佳模型的训练历史\n",
    "\n",
    "best_history = best_result_row['history']\n",
    "\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "# 准确率图\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(best_history['accuracy'], label='训练准确率')\n",
    "plt.plot(best_history['val_accuracy'], label='测试准确率')\n",
    "plt.title('最佳模型 - 准确率')\n",
    "plt.xlabel('迭代次数')\n",
    "plt.ylabel('准确率')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "\n",
    "# 损失图\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(best_history['loss'], label='训练损失')\n",
    "plt.plot(best_history['val_loss'], label='测试损失')\n",
    "plt.title('最佳模型 - 损失 (交叉熵)')\n",
    "plt.xlabel('迭代次数')\n",
    "plt.ylabel('损失')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "\n",
    "plt.suptitle(f\"最佳模型性能: {best_result_row['features']} | LR={best_result_row['learning_rate']} | Epochs={best_result_row['epochs']}\", fontsize=14)\n",
    "plt.tight_layout(rect=[0, 0.03, 1, 0.95]) # 调整布局以避免标题重叠\n",
    "plt.savefig('output/best_model_training_history.png')\n",
    "plt.show() # 在Jupyter中显示图形\n",
    "print(\"\\n最佳模型的训练历史图已保存到 output/best_model_training_history.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f6f2c798",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "不同特征组合的性能对比图已保存到 output/feature_combination_performance.png\n"
     ]
    }
   ],
   "source": [
    "#可视化不同特征组合的性能对比\n",
    "\n",
    "# 计算每个特征组合的平均测试准确率，并按降序排序\n",
    "avg_performance = results_df.groupby('features')['test_accuracy'].mean().sort_values(ascending=False)\n",
    "\n",
    "plt.figure(figsize=(12, 7))\n",
    "avg_performance.plot(kind='bar', color=plt.cm.viridis(np.linspace(0, 1, len(avg_performance))))\n",
    "plt.title('不同特征组合的平均测试准确率')\n",
    "plt.xlabel('特征组合')\n",
    "plt.ylabel('平均测试准确率')\n",
    "plt.xticks(rotation=45, ha='right') # 旋转x轴标签以便阅读\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.tight_layout() # 自动调整布局\n",
    "plt.savefig('output/feature_combination_performance.png')\n",
    "plt.show() # 在Jupyter中显示图形\n",
    "print(\"不同特征组合的性能对比图已保存到 output/feature_combination_performance.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4da19f9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- 各特征组合下的最佳结果总结 (表格形式) ---\n"
     ]
    },
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>learning_rate</th>\n",
       "      <th>epochs</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>test_loss</th>\n",
       "      <th>test_accuracy</th>\n",
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       "      <th>74</th>\n",
       "      <td>sepal_length+petal_length+petal_width</td>\n",
       "      <td>0.010</td>\n",
       "      <td>150</td>\n",
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       "      <td>0.033995</td>\n",
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       "    <tr>\n",
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       "      <td>0.162687</td>\n",
       "      <td>0.958333</td>\n",
       "      <td>0.161497</td>\n",
       "      <td>0.966667</td>\n",
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       "      <th>90</th>\n",
       "      <td>sepal_length+sepal_width+petal_length+petal_width</td>\n",
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       "      <td>0.010</td>\n",
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       "      <td>0.983333</td>\n",
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       "      <td>0.966667</td>\n",
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       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>sepal_length+sepal_width+petal_width</td>\n",
       "      <td>0.010</td>\n",
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       "      <td>0.090161</td>\n",
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      ],
      "text/plain": [
       "                                             features  learning_rate  epochs  \\\n",
       "74              sepal_length+petal_length+petal_width          0.010     150   \n",
       "35                           sepal_width+petal_length          0.001     150   \n",
       "45                           petal_length+petal_width          0.010      50   \n",
       "9                           sepal_length+petal_length          0.010      50   \n",
       "18                           sepal_length+petal_width          0.010      50   \n",
       "90  sepal_length+sepal_width+petal_length+petal_width          0.010      50   \n",
       "54              sepal_length+sepal_width+petal_length          0.010      50   \n",
       "81               sepal_width+petal_length+petal_width          0.010      50   \n",
       "63               sepal_length+sepal_width+petal_width          0.010      50   \n",
       "36                            sepal_width+petal_width          0.010      50   \n",
       "0                            sepal_length+sepal_width          0.010      50   \n",
       "\n",
       "    train_loss  train_accuracy  test_loss  test_accuracy  \n",
       "74    0.062569        0.975000   0.033995       1.000000  \n",
       "35    0.182823        0.950000   0.181431       1.000000  \n",
       "45    0.081226        0.958333   0.070447       0.966667  \n",
       "9     0.097615        0.975000   0.070929       0.966667  \n",
       "18    0.162687        0.958333   0.161497       0.966667  \n",
       "90    0.035292        0.983333   0.049674       0.966667  \n",
       "54    0.080390        0.975000   0.057827       0.966667  \n",
       "81    0.043263        0.983333   0.062126       0.966667  \n",
       "63    0.078300        0.966667   0.090161       0.966667  \n",
       "36    0.092427        0.950000   0.096592       0.966667  \n",
       "0     0.327356        0.841667   0.421383       0.800000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cell 9: 各特征组合下的最佳结果总结\n",
    "\n",
    "# 针对每个特征组合，找出测试准确率最高的那个实验\n",
    "best_per_feature = results_df.loc[results_df.groupby('features')['test_accuracy'].idxmax()]\n",
    "best_per_feature = best_per_feature.sort_values('test_accuracy', ascending=False)\n",
    "\n",
    "print(\"\\n--- 各特征组合下的最佳结果总结 (表格形式) ---\")\n",
    "display(best_per_feature[[ \n",
    "    'features', 'learning_rate', 'epochs', 'train_loss', \n",
    "    'train_accuracy', 'test_loss', 'test_accuracy'\n",
    "]])"
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